DOI: 10.1016/j.marpolbul.2017.04.022
Scopus记录号: 2-s2.0-85018634083
论文题名: Real-time eutrophication status evaluation of coastal waters using support vector machine with grid search algorithm
作者: Kong X. ; Sun Y. ; Su R. ; Shi X.
刊名: Marine Pollution Bulletin
ISSN: 0025-326X
EISSN: 1879-3363
出版年: 2017
卷: 119, 期: 1 起始页码: 307
结束页码: 319
语种: 英语
英文关键词: CDOM
; Easily measured parameters
; Eutrophication assessment
; Fluorescence
; Support vector machine
; TRIX
Scopus关键词: Classification (of information)
; Fluorescence
; Support vector machines
; CDOM
; Classification accuracy
; Eutrophication assessment
; Grid-search algorithm
; Measured parameters
; Predictive performance
; Real time monitoring
; TRIX
; Eutrophication
; algorithm
; coastal water
; dissolved organic matter
; environmental monitoring
; eutrophication
; fluorescence
; support vector machine
; coastal waters
; eutrophication
; fluorescence
; model
; support vector machine
; validation process
; algorithm
; sea water
; Algorithms
; Eutrophication
; Seawater
; Support Vector Machine
Scopus学科分类: Agricultural and Biological Sciences: Aquatic Science
; Earth and Planetary Sciences: Oceanography
; Environmental Science: Pollution
英文摘要: The development of techniques for real-time monitoring of the eutrophication status of coastal waters is of great importance for realizing potential cost savings in coastal monitoring programs and providing timely advice for marine health management. In this study, a GS optimized SVM was proposed to model relationships between 6 easily measured parameters (DO, Chl-a, C1, C2, C3 and C4) and the TRIX index for rapidly assessing marine eutrophication states of coastal waters. The good predictive performance of the developed method was indicated by the R2 between the measured and predicted values (0.92 for the training dataset and 0.91 for the validation dataset) at a 95% confidence level. The classification accuracy of the eutrophication status was 86.5% for the training dataset and 85.6% for the validation dataset. The results indicated that it is feasible to develop an SVM technique for timely evaluation of the eutrophication status by easily measured parameters. © 2017 Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/87582
Appears in Collections: 过去全球变化的重建 全球变化的国际研究计划
There are no files associated with this item.
作者单位: Key Laboratory of Marine Chemistry Theory and Technology, Ministry of Education Ocean University of China, Qingdao, China
Recommended Citation:
Kong X.,Sun Y.,Su R.,et al. Real-time eutrophication status evaluation of coastal waters using support vector machine with grid search algorithm[J]. Marine Pollution Bulletin,2017-01-01,119(1)